Optical Network Expert Series: Guide to improving future network economics with near-zero margin networking
Over the past several years, coherent technology advancements have enabled providers to meet increasing capacity demands while continuing to lower the cost per bit of transport. However, as we get closer and closer to the Shannon Limit, the gains we are accustomed to obtaining with each generation of coherent technology are diminishing. In this educational blog series, you will learn directly from our Ciena experts about an innovative approach that is being considered for the economic viability of future optical networks: maximizing the delivered capacity of optical transponders by mining the SNR margin to a near-zero level. Our experts, David Boertjes and David Cote share their insights on the design and deployment considerations behind this approach, from the design of the equipment to leveraging analytics and AI capabilities, all of which are important components that contribute to delivering a practical near-zero margin network sooner than one may think.
Ciena’s Paulina Gomez introduces our "Optical Network Expert Series: A guide to improving future network economics with near-zero margin networking" blog series. She explains what you will learn from our experts working to develop the hardware and software technology advancements required to achieve a near-zero margin network.
Ciena’s optical network expert, David Boertjes discusses the programmable infrastructure needed to achieve a near-zero margin network, including the key attributes of flexible line equipment and programmable coherent modems. He also explains how these components are evolving to enable safe network operation at near-zero margin.
In the third part of the series, David Boertjes explains how analytics can help monitor and control the SNR margin , and David Côté explains how machine learning can be applied to fiber characterization for optimal system performance.
In the final blog of our series, David Côté explains how artificial intelligence (AI) is being applied to maximize network capacity and quality of transmission (QoT).